Chapter 1
Scalable Indexing for Big Data Processing
Hisham Mohamed and Stéphane Marchand-Maillet
Abstract
The K-nearest neighbor (K-NN) search problem is the way to find and predict the closest and most similar objects to a given query. It finds many applications for information retrieval and visualization, machine learning, and data mining. The context of Big Data imposes the finding of approximate solutions. Permutation-based indexing is one of the most recent techniques for approximate similarity search in large-scale domains. Data objects are represented by a list of references (pivots), which are ordered with respect to their distances from the object. In this chapter, we show different distributed algorithms for efficient indexing and ...
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